Micro-aerial vehicles, such as rotorcraft micro-aerial vehicles, are capable of flying autonomously. Accurate autonomous flight can be achieved provided that there is sufficient sensor data available to provide control input for the autonomous flight. For example, in some outdoor environments where a global positioning system (GPS) is available, autonomous flight can be achieved based on GPS signals. However, in environments where GPS is not available, such as indoor environments and even outdoor urban environments, autonomous flight based on GPS alone is not possible. In some indoor environments, magnetometer output may not be available or reliable due to magnetic interference caused by structures. Thus, reliance on a single modality of sensor to control flight of a rotorcraft MAV may not be desirable.
Another goal of controlling autonomous flight of a rotorcraft MAV is smooth transition between states when a sensor modality that was not previously available becomes available. For example, when a rotorcraft MAV is flying indoors where GPS is not available and then transitions to an outdoor environment where GPS suddenly becomes available, the rotorcraft may determine that it is far off course and may attempt to correct the error by immediately moving to be on course. It is desirable that such transitions be smooth, rather than having the rotorcraft immediately make large changes in velocity and trajectory to get back on course.
Multiple types of sensor data are available to control autonomous flight in rotorcraft micro-aerial vehicles. For example, onboard cameras, laser scanners, GPS transceivers, and accelerometers can provide multiple inputs that are suitable as control inputs for controlling flight. However, as stated above, relying on any one of these sensors fails when the assumptions associated with the sensor fails. Because each type of sensor produces a unique kind of output with a unique level of uncertainty in its measurement, there exists a need for improved methods, systems, and computer readable media for multi-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft MAV.